Real-Time Facial Emotion Detection Application with Image Processing Based on Convolutional Neural Network (CNN)

Hakim, Ghaeril Juniawan Parel
Simangunsong, Gandi Abetnego
Rangga Wasita Ningrat
Jonathan Cristiano Rabika
Muhammad Rafi' Rusafni
Endang Purnama Giri
Gema Parasti Mindara

International Journal of Electrical Engineering, Mathematics and Computer Science
Vol. 1 No. 4 (2024): December : International Journal of Electrical Engineering, Mathematics and Com

Facial Emotion Recognition (FER) is a key technology for identifying emotions based on facial expressions, with applications in human-computer interaction, mental health monitoring, and customer analysis. This study presents the development of a real-time emotion recognition system using Convolutional Neural Networks (CNNs) and OpenCV, addressing challenges such as varying lighting and facial occlusions. The system, trained on the FER2013 dataset, achieved 85% accuracy in emotion classification, demonstrating high performance in detecting happiness, sadness, and surprise. The results highlight the system's effectiveness in real-time applications, offering potential for use in mental health and customer behavior analysis.